@inproceedings{bechet-etal-2019-calor-quest,
title = "{CALOR}-{QUEST} : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations",
author = "Bechet, Frederic and
Aloui, Cindy and
Charlet, Delphine and
Damnati, Geraldine and
Heinecke, Johannes and
Nasr, Alexis and
Herledan, Frederic",
editor = "Fisch, Adam and
Talmor, Alon and
Jia, Robin and
Seo, Minjoon and
Choi, Eunsol and
Chen, Danqi",
booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5803",
doi = "10.18653/v1/D19-5803",
pages = "19--26",
abstract = "Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document. Recently very large corpora (SQuAD, MS MARCO) containing triplets (document, question, answer) were made available to the scientific community to develop supervised methods based on deep neural networks with promising results. These methods need very large training corpus to be efficient, however such kind of data only exists for English and Chinese at the moment. The aim of this study is the development of such resources for other languages by proposing to generate in a semi-automatic way questions from the semantic Frame analysis of large corpora. The collect of natural questions is reduced to a validation/test set. We applied this method on the CALOR-Frame French corpus to develop the CALOR-QUEST resource presented in this paper.",
}
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<abstract>Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document. Recently very large corpora (SQuAD, MS MARCO) containing triplets (document, question, answer) were made available to the scientific community to develop supervised methods based on deep neural networks with promising results. These methods need very large training corpus to be efficient, however such kind of data only exists for English and Chinese at the moment. The aim of this study is the development of such resources for other languages by proposing to generate in a semi-automatic way questions from the semantic Frame analysis of large corpora. The collect of natural questions is reduced to a validation/test set. We applied this method on the CALOR-Frame French corpus to develop the CALOR-QUEST resource presented in this paper.</abstract>
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%0 Conference Proceedings
%T CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations
%A Bechet, Frederic
%A Aloui, Cindy
%A Charlet, Delphine
%A Damnati, Geraldine
%A Heinecke, Johannes
%A Nasr, Alexis
%A Herledan, Frederic
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F bechet-etal-2019-calor-quest
%X Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document. Recently very large corpora (SQuAD, MS MARCO) containing triplets (document, question, answer) were made available to the scientific community to develop supervised methods based on deep neural networks with promising results. These methods need very large training corpus to be efficient, however such kind of data only exists for English and Chinese at the moment. The aim of this study is the development of such resources for other languages by proposing to generate in a semi-automatic way questions from the semantic Frame analysis of large corpora. The collect of natural questions is reduced to a validation/test set. We applied this method on the CALOR-Frame French corpus to develop the CALOR-QUEST resource presented in this paper.
%R 10.18653/v1/D19-5803
%U https://aclanthology.org/D19-5803
%U https://doi.org/10.18653/v1/D19-5803
%P 19-26
Markdown (Informal)
[CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations](https://aclanthology.org/D19-5803) (Bechet et al., 2019)
ACL